Fraunhofer Center for Assistive Information and Communication Solutions – AICOS
10.02.2022
Fraunhofer Portugal was recently granted a European patent (EP3758023B1) on a method and device for assessing the physical movement of an operator at manufacturing workstations.
The computer-implemented method was invented by FhP-AICOS researchers Duarte Folgado, Marília Barandas, Prof. Hugo Gamboa, and Prof. Miguel Ângelo Carvalho from University of Minho. The invention relates to the field of human motion analysis and discloses an automated method and device to measure the operator’s performance and ergonomic risk at manufacturing workstations.
To extract the desired insights, operators use a wearable sensor with an inertial measurement unit that transmits data to a centralized hub to collect information from different workers and a processing node to compute relevant information from the collected data. The invention relies on a template motion to segment a series of repetitive motion of working cycles. The proposed segmentation approach is an adaptation of the state-of-the-art Dynamic Time Warping algorithm for time series that increases its performance.
This invention is currently being used in the Operator project, an MIT Portugal Flagship Project that aims to create tools to ensure the well-being (physical and mental) of an operator in industrial settings. In the Operator, FhP-AICOS is responsible for user research, the technological development of wearables, algorithms for motion analysis, and the digital platform that enables gathering and crossing quantitative and qualitative data, building a richer picture of the workers’ well-being and enabling better predictive models.
The request for the patent was submitted in July of 2020, approved in January of 2022 and published yesterday, on February, 9th.
“Receiving the news that our European patent application was granted to our invention is an important milestone to our research over the last few years. The patent discloses new methods to extract knowledge from human motion data in the context of manufacturing lines. This achievement is also a significant step to deploying in industrial lines new technology to support the operators.”
Duarte Folgado, Marília Barandas and Prof. Hugo Gamboa